Last updated: 2020-03-07
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Knit directory: Comparative_APA/analysis/
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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
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File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | c6a11f0 | brimittleman | 2020-03-07 | add expression indep |
html | 22926ec | brimittleman | 2020-03-06 | Build site. |
Rmd | 680d435 | brimittleman | 2020-03-06 | add ptm analysis |
library(ggpubr)
Loading required package: ggplot2
Loading required package: magrittr
library(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ tibble 2.1.1 ✔ purrr 0.3.2
✔ tidyr 0.8.3 ✔ dplyr 0.8.0.1
✔ readr 1.3.1 ✔ stringr 1.3.1
✔ tibble 2.1.1 ✔ forcats 0.3.0
── Conflicts ───────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ tidyr::extract() masks magrittr::extract()
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
✖ purrr::set_names() masks magrittr::set_names()
Looking at protien interactions. https://downloads.thebiogrid.org/BioGRID/Release-Archive/BIOGRID-3.5.182/
Are genes with dAPA more likely to be in genes with known protein protein interactions. Given the regulatory elements in the 3’ UTR. This could help us understand a mechanism.
( interactions, chemical associations, and post-translational modifications (PTM))
mkdir ../data/bioGRID
Look at data:
Fix colnames
Biogrid=read_tsv("../data/bioGRID/BIOGRID-ORGANISM-Homo_sapiens-3.5.182.tab2.txt")
Parsed with column specification:
cols(
.default = col_character(),
`#BioGRID Interaction ID` = col_double(),
`Entrez Gene Interactor A` = col_double(),
`Entrez Gene Interactor B` = col_double(),
`BioGRID ID Interactor A` = col_double(),
`BioGRID ID Interactor B` = col_double(),
`Pubmed ID` = col_double(),
`Organism Interactor A` = col_double(),
`Organism Interactor B` = col_double()
)
See spec(...) for full column specifications.
colnames(Biogrid)= c( "BioGRID_Interaction_ID", "Entrez_Gene_Interactor_A", "Entrez_Gene_Interactor_B", "BioGRID_ID_Interactor_A", "BioGRID_ID_Interactor_B" , "Systematic_Name_Interactor_A","Systematic_Name_Interactor_B", "Official_Symbol_Interactor_A", "Official_Symbol_Interactor_B","Synonyms_Interactor_A", "Synonyms_Interactor_B" , "Experimental_System", "Experimental_System_Type" ,"Author" , "Pubmed_ID" ,"Organism_Interactor_A", "Organism_Interactor_B", "Throughput","Score", "Modification" , "Phenotypes","Qualifications", "Tags" , "Source Database" )
Select the official names for the interactors:
Biogridsmall=Biogrid %>% dplyr::select(Official_Symbol_Interactor_A, Official_Symbol_Interactor_B,Score, Modification, Phenotypes, Tags)
I need a way to remove duplicates. I can do this by making unordered sets of these. I will need the uniq sets.
Make a set with the pasted version of A:B and B:A, keep the unique set
BioGridsets=Biogridsmall %>% mutate(Afirst=paste(Official_Symbol_Interactor_A, Official_Symbol_Interactor_B, sep="_:_"), Bfirst=paste(Official_Symbol_Interactor_B, Official_Symbol_Interactor_A, sep="_:_"))
Allsets= as.data.frame(c(BioGridsets$Afirst, BioGridsets$Bfirst)) %>% unique()
colnames(Allsets)=c("Interaction")
AllGenes=as.data.frame(c(Biogridsmall$Official_Symbol_Interactor_A, Biogridsmall$Official_Symbol_Interactor_B)) %>% unique()
colnames(AllGenes)=c("Genes")
I want to know if my genes are in either of these sets. I also need the set of all genes that are involved.
Allsets_sep= Allsets %>% separate(Interaction, into=c("a","b"), sep="_:_")
Get all of the genes together in one column (not unique) , group by the gene and count how many interactions
GenesWint= as.data.frame(c(Allsets_sep$a, Allsets_sep$b))
colnames(GenesWint)= c("gene")
GenesWint_g= GenesWint %>% group_by(gene) %>% summarise(nInt=n())
Join this with the genes I test.
NucRes=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% group_by(gene, SigPAU2) %>% summarise(N=n()) %>% spread(SigPAU2,N) %>% replace_na(list(Yes=0)) %>% mutate(dAPA=ifelse(Yes>=1, "Yes", "No")) %>% dplyr::select(-Yes, -No)
NucResAll= NucRes %>% left_join(GenesWint_g, by="gene") %>% replace_na(list(nInt=0))
Warning: Column `gene` joining character vector and factor, coercing into
character vector
write.table(GenesWint_g, "../data/bioGRID/GeneswInteractions.txt",col.names = T, row.names = F, quote = F)
ggplot(NucResAll,aes(x=dAPA, y=log10(nInt +1))) + geom_boxplot() + stat_compare_means()
Version | Author | Date |
---|---|---|
22926ec | brimittleman | 2020-03-06 |
Does not look like this can explain the differences.
Enriched for non 0?
NucResAll_g= NucResAll %>% mutate(HasInteraction=ifelse(nInt>0, "Yes", "No")) %>% group_by(dAPA, HasInteraction) %>% summarise(nWithSet=n())
NucResAll_g
# A tibble: 4 x 3
# Groups: dAPA [2]
dAPA HasInteraction nWithSet
<chr> <chr> <int>
1 No No 814
2 No Yes 5581
3 Yes No 280
4 Yes Yes 1869
5581/(5581+814)
[1] 0.8727131
1869/(1869+280)
[1] 0.8697068
Doesnt look like differentail are more likely to have an interaction.
Biogridsmall %>% dplyr::select(Modification) %>% unique()
# A tibble: 19 x 1
Modification
<chr>
1 -
2 No Modification
3 Proteolytic Processing
4 Phosphorylation
5 Ubiquitination
6 Methylation
7 Dephosphorylation
8 Sumoylation
9 Deacetylation
10 Acetylation
11 Glycosylation
12 Deubiquitination
13 Demethylation
14 Ribosylation
15 Desumoylation
16 Nedd(Rub1)ylation
17 Deneddylation
18 FAT10ylation
19 Neddylation
sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] forcats_0.3.0 stringr_1.3.1 dplyr_0.8.0.1 purrr_0.3.2
[5] readr_1.3.1 tidyr_0.8.3 tibble_2.1.1 tidyverse_1.2.1
[9] workflowr_1.6.0 ggpubr_0.2 magrittr_1.5 ggplot2_3.1.1
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 haven_1.1.2 lattice_0.20-38 colorspace_1.3-2
[5] generics_0.0.2 htmltools_0.3.6 yaml_2.2.0 utf8_1.1.4
[9] rlang_0.4.0 later_0.7.5 pillar_1.3.1 glue_1.3.0
[13] withr_2.1.2 modelr_0.1.2 readxl_1.1.0 plyr_1.8.4
[17] munsell_0.5.0 gtable_0.2.0 cellranger_1.1.0 rvest_0.3.2
[21] evaluate_0.12 labeling_0.3 knitr_1.20 httpuv_1.4.5
[25] fansi_0.4.0 broom_0.5.1 Rcpp_1.0.2 promises_1.0.1
[29] scales_1.0.0 backports_1.1.2 jsonlite_1.6 fs_1.3.1
[33] hms_0.4.2 digest_0.6.18 stringi_1.2.4 grid_3.5.1
[37] rprojroot_1.3-2 cli_1.1.0 tools_3.5.1 lazyeval_0.2.1
[41] crayon_1.3.4 whisker_0.3-2 pkgconfig_2.0.2 xml2_1.2.0
[45] lubridate_1.7.4 assertthat_0.2.0 rmarkdown_1.10 httr_1.3.1
[49] rstudioapi_0.10 R6_2.3.0 nlme_3.1-137 git2r_0.26.1
[53] compiler_3.5.1